Forecasting Inflation Rate by Star Model: An Indian Experience

2002 ◽  
Vol 53 (3-4) ◽  
pp. 265-288
Author(s):  
G.P. Samanta
Keyword(s):  
Inflation Rate ◽  
Wholesale Price ◽  
Smooth Transition ◽  
Forecast Errors ◽  
Star Model ◽  
Star Models ◽  
Non Linear ◽  
Out Of Sample ◽  
Short Run ◽  
Forecasting Inflation

In this empirical study, an attempt has been made to model non-linear dynamics of inflation rate in India through Smooth Transition ⁄ Threshold Auto-Regression (STAR). Inflation is measured based on weekly data on Wholesale Price Index (WPI) fur a period of seven years from the week ended April 2, 1994 to the week ended March 31, 2001. The log(WPI) series is detected to be a Difference-Stationary process, indicating that the series is non-stationary but its first-order difference is stationary. The generating process of the transformed-stationary series is identified to be non-linear. Six variants of STAR model are estimated for transformed-stationary series and are used to forecast WPI and annual inflation rate. Empirical assessment of out-of-sample forecast errors eveals that estimated STAR models perform reasonably well in generating short-run forecasts of both the variables.

scholarly journals Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

2019 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
Fuzuli Aliyev
Keyword(s):  
Random Walk ◽  
Market Efficiency ◽  
Financial Time Series ◽  
Weak Form ◽  
Ar Model ◽  
Smooth Transition ◽  
Star Model ◽  
Forecast Performance ◽  
Star Models ◽  
Out Of Sample

Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.

scholarly journals Genetic Algorithm Based Improved ESTAR Nonlinear Models for Modelling Sunspot Numbers and Global Temperatures

2021 ◽  
Author(s):  
Bishal Gurung ◽  
Achal Lama ◽  
Santosha Rathod ◽  
K N Singh
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Models ◽  
Forecast Error ◽  
Optimization Procedure ◽  
Smooth Transition ◽  
Star Model ◽  
Estimation Of Parameters ◽  
Sunspot Numbers ◽  
Star Models ◽  
One Step

Abstract Smooth Transition Autoregressive (STAR) models are employed to describe cyclical data. As estimation of parameters of STAR using nonlinear methods was time-consuming, Genetic algorithm (GA), a powerful optimization procedure was applied for the same. Further, optimal one step and two step ahead forecasts along with their forecast error variances are derived theoretically for fitted STAR model using conditional expectations. Given the importance of the issue of global warming, the current paper aims to model the sunspot numbers and global mean temperatures. Further, appropriate tests are carried out to see if the model employed is appropriate for the datasets.

P-star model for India: a nonlinear approach

2018 ◽  
Vol 22 (5) ◽  
Author(s):  
Aditi Chaubal
Keyword(s):  
Wholesale Price ◽  
Output Gap ◽  
Star Model ◽  
Vector Error Correction ◽  
Nonlinear Approach ◽  
Long Run ◽  
Wholesale Price Index ◽  
Short Run ◽  
Gap Models ◽  
Long Run Equilibrium

Abstract Inflation in India has been a major cause for concern in the recent past (2008–2012). This study examines the Indian wholesale price index inflation from 1951 to 2012 using P-star (or P*) models after accounting for the nonlinearities in the data by establishing the presence of a nonlinear long-run equilibrium. The paper establishes the presence of a threshold vector error correction model (TVECM) between prices and their long-run equilibrium with three optimal regimes to explain the short-run and long-run dynamics based on an error correcting transition term. Based on these results, the study classifies the various regimes that Indian inflation goes through based on historical economic events. The P* models (price gap, output gap and velocity gap models) were implemented regime-wise. The price gap models (output gap and income velocity gap determine inflation) were found to be optimal in the first and second regimes and consistent with theory. The velocity gap model (which has monetarist foundations) was found to be optimal in the third regime.

Modeling Multiple Regimes in the Business Cycle

1999 ◽  
Vol 3 (3) ◽  
pp. 311-340 ◽  
Author(s):  
Dick van Dijk ◽  
Philip Hans Franses
Keyword(s):  
Business Cycle ◽  
Growth Phase ◽  
High Growth ◽  
Smooth Transition ◽  
Star Model ◽  
Two Phase ◽  
Star Models ◽  
Moderate Growth ◽  
Multiple Regimes ◽  
The Business Cycle

The interest in business-cycle asymmetry has been steadily increasing over the past 15 years. Most research has focused on the different behavior of macroeconomic variables during expansions and contractions, which by now is well documented. Recent evidence suggests that such a two-phase characterization of the business cycle might be too restrictive. In particular, it might be worthwhile to decompose the recovery phase in a high-growth phase (immediately following the trough of a cycle) and a subsequent moderate-growth phase. The issue of multiple regimes in the business cycle is addressed using smooth-transition autoregressive (STAR) models. A possible limitation of STAR models as they currently are used is that essentially they deal with only two regimes. We propose a generalization of the STAR model such that more than two regimes can be accommodated. It is demonstrated that the class of multiple-regime STAR (MRSTAR) models can be obtained from the two-regime model in a simple way. The main properties of the MRSTAR model and several issues that are relevant for empirical specification are discussed in detail. In particular, a Lagrange multiplier-type test is derived that can be used to determine the appropriate number of regimes. A limited simulation study indicates its practical usefulness. Application of the new model class to U.S. real GNP provides evidence in favor of the existence of multiple business-cycle phases.

scholarly journals Determinants of Non-Linear Effects of Fiscal Policy on Output: The Case of Bulgaria

2009 ◽  
Vol 4 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Vladimir Vladimirov ◽  
Maria Neycheva
Keyword(s):  
Fiscal Policy ◽  
Currency Board ◽  
Main Determinant ◽  
The Real ◽  
Real Gdp ◽  
Non Linear ◽  
Short Run ◽  
Linear Effects ◽  
Policy Impacts ◽  
The Government

Determinants of Non-Linear Effects of Fiscal Policy on Output: The Case of BulgariaThe paper illuminates the non-linear effects of the government budget on short-run economic activity. The study shows that in the Bulgarian economy under a Currency Board Arrangement the tax policy impacts the real growth in the standard Keynesian manner. On the other hand, the expenditure policy exhibits non-Keynesian behavior on the short-run output: cuts in government spending accelerate the real GDP growth. The main determinant of this outcome is the size of the discretionary budgetary changes. The results imply that the balanced budget rule improves the sustainability of public finances without assuring a growth-enhancing effect.

Anticipating revisions in the transportation services index

2021 ◽  
pp. 1-13
Author(s):  
Tucker S. McElroy ◽  
Anindya Roy ◽  
James Livsey ◽  
Theresa Firestine ◽  
Ken Notis
Keyword(s):  
Confidence Intervals ◽  
Vector Autoregression ◽  
Data Availability ◽  
Economic Conditions ◽  
Forecast Errors ◽  
Release Date ◽  
Transportation Services ◽  
Publication Delay ◽  
Source Data ◽  
Out Of Sample

The Transportation Services Index (TSI) lags two months from its release date due to source data availability, and it is desirable to publish a preliminary TSI that is advanced two months ahead. We model and forecast TSI with a co-integrated Vector Autoregression, also considering two explanatory series that do not have publication delay. Thus we are able to produce forecasts and nowcasts of the index, and we demonstrate that – during normal economic conditions – out-of-sample performance is within the scope expected by the forecast confidence intervals. We also examine the performance of the models at the onset of the COVID-19 pandemic, and the large forecast errors at this regime change are beyond the bounds indicated by our model. The practical ramifications of this methodology is discussed.

scholarly journals Mathematical Development and Evaluation of Forecasting Models for Accuracy of Inflation in Developing Countries: A Case of Vietnam

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Nhu-Ty Nguyen ◽  
Thanh-Tuyen Tran
Keyword(s):  
Raw Materials ◽  
Arima Model ◽  
Consumer Price Index ◽  
Forecast Accuracy ◽  
Complex Problem ◽  
Forecast Performance ◽  
Forecasting Models ◽  
Out Of Sample ◽  
Forecasting Inflation ◽  
Using Data

Inflation is a key element of a national economy, and it is also a prominent and important issue influencing the whole economy in terms of marketing. This is a complex problem requiring a large investment of time and wisdom to attain positive results. Thus, appropriate tools for forecasting inflation variables are crucial significant for policy making. In this study, both clarified value calculation and use of a genetic algorithm to find the optimal parameters are adopted simultaneously to construct improved models: ARIMA, GM(1,1), Verhulst, DGM(1,1), and DGM(2,1) by using data of Vietnamese inflation output from January 2005 to November 2013. The MAPE, MSE, RMSE, and MAD are four criteria with which the various forecasting models results are compared. Moreover, to see whether differences exist, Friedman and Wilcoxon tests are applied. Both in-sample and out-of-sample forecast performance results show that the ARIMA model has highly accurate forecasting in Raw Materials Price (RMP) and Gold Price (GP), whereas, the calculated results of GM(1,1) and DGM(1,1) are suitable to forecast Consumer Price Index (CPI). Therefore, the ARIMA, GM(1,1), and DGM(1,1) can handle the forecast accuracy of the issue, and they are suitable in modeling and forecasting of inflation in the case of Vietnam.

scholarly journals The spatial integration of potato wholesale markets of Uttarakhand in India

2017 ◽  
Vol 14 (2) ◽  
pp. 20-30 ◽  
Author(s):  
A Kumar ◽  
R Mishra
Keyword(s):  
Wholesale Price ◽  
Likelihood Method ◽  
Spatial Integration ◽  
Market Information ◽  
Demand And Supply ◽  
Vector Error Correction ◽  
Vector Error ◽  
Long Run ◽  
Short Run ◽  
Price Demand

This paper analyzes the spatial integration of potato markets in Uttarakhand using monthly wholesale price for ten years. The maximum likelihood method of cointegration developed by Johansen (1988) was used in the study. The dynamics of short-run price responses were examined using vector error correction model (VECM). The results indicated that five potato markets reacted on the long-run cointegrating equations while the speed of price adjustment in the short-run was almost absent. Moreover, it was found that the longer the distance between the markets, the weaker the integration was. To increase the efficiency of potato markets in Uttarakhand, there is need to focus on building an improved market information system. This system should be able to disseminate timely market information about price, demand and supply of produce to enable producers, traders and consumers to make proper production and marketing decisions.SAARC J. Agri., 14(2): 20-30 (2016)

scholarly journals Impact of Exchange Rate Management on the Nigerian Economic Growth: Empirical Validation

2019 ◽  
Vol 1 (2) ◽  
pp. 28-35
Author(s):  
Friday Osaru Ovenseri Ogbomo ◽  
Precious Imuwahen Ajoonu
Keyword(s):  
Economic Growth ◽  
Exchange Rate ◽  
Inflation Rate ◽  
Government Expenditure ◽  
Least Square ◽  
Ordinary Least Square ◽  
Exchange Rate Management ◽  
Long Run ◽  
Short Run ◽  
The Impact

This paper examined the impact of Exchange Rate Management on economic growth in Nigeria between 1980 and 2015. The study was set to gauge how the management of exchange rate in Nigeria has impacted the economy. The study employed the Ordinary Least Square (OLS) method in its analysis. Co-integration and Error Correction Techniques were used to establish the Short-run and Long-run relationships between economic growth and other relevant economic indicators. The result revealed that exchange rate management proxy by various exchange rates regimes in Nigeria was not germane to economic growth. Rather, government expenditure, inflation rate, money supply and foreign direct investment significantly impact on economic growth in Nigeria. It is against this backdrop that the Nigerian economy must diversify her export base to create room for more inflow of foreign exchange.  

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